1. Field of the Invention
The present invention relates to control mechanisms for robotic devices, and more particularly to a time- and energy-optimized control method for mobile parallel manipulators.
2. Description of the Related Art
U.S. Patent Application Publication Number 2012/0290131, titled “PARALLEL KINEMATIC MACHINE TRAJECTORY PLANNING METHOD” and published Nov. 15, 2012, which is hereby incorporated by reference in its entirety, discloses a parallel kinematic machine (PKM) trajectory planning method operable via a data-driven neuro-fuzzy multistage-based system. Offline planning based on robot kinematic and dynamic models, including actuators, was performed to generate a large dataset of trajectories covering most of the robot workspace and minimizing time and energy, while avoiding singularities and limits on joint angles, rates, accelerations and torques. The method implements an augmented Lagrangian solver on a decoupled form of the PKM dynamics in order to solve the resulting non-linear constrained optimal control problem. Using outcomes of the offline-planning, the data-driven neuro-fuzzy inference system was built to learn, to capture, and to optimize the desired dynamic behavior of the PKM. While the above method works for stationary machines, there remains the problem of planning a trajectory for a mobile parallel kinematic machine (MPM).
Thus, a control method for mobile parallel manipulators solving the aforementioned problems is desired.